figure = plt.figure(figsize=(20, 20))
plt.title('青岛地铁线路和青岛市2021年GDP及7普常住人口数据', fontsize=16)
plt.axis('off')
axes1 = figure.add_subplot(2,2,1)
axes2 = figure.add_subplot(2,2,2)
axes3 = figure.add_subplot(2,2,3)
axes4 = figure.add_subplot(2,2,4)
#叠加GDP
data_qingdao.plot(ax=axes1, column='GDP', alpha=0.5, edgecolor='k',
categorical=False, legend=True, legend_kwds={'loc': 'lower right'},
scheme='User_Defined', classification_kwds=dict(bins=[1000,2000,3000,4000,5000]),
cmap='OrRd', linewidth=1)
for ctype, data in metro_data_qingdao_all.groupby('name'):
color = Lines_Palette[ctype]
data.plot(ax=axes1, color=color, label=ctype, linewidth=0.5)
cx.add_basemap(ax=axes1, source='https://stamen-tiles-a.a.ssl.fastly.net/terrain-background/{z}/{x}/{y}.png', zoom=8)
axes1.add_artist(ScaleBar(1))
axes1.axis('off')
axes1.set_title('GDP (亿元)')
#叠加人均GDP
data_qingdao.plot(ax=axes2, column='人均GDP', alpha=0.5, edgecolor='k',
categorical=False, legend=True, legend_kwds={'loc': 'lower right'},
scheme='User_Defined', classification_kwds={'bins':[10, 15, 20, 25, 30]},
cmap='OrRd', linewidth=1)
for ctype, data in metro_data_qingdao_all.groupby('name'):
color = Lines_Palette[ctype]
data.plot(ax=axes2, color=color, label=ctype, linewidth=0.5)
cx.add_basemap(ax=axes2, source='https://stamen-tiles-a.a.ssl.fastly.net/terrain-background/{z}/{x}/{y}.png', zoom=8)
axes2.add_artist(ScaleBar(1))
axes2.axis('off')
axes2.set_title('人均GDP (万元)')
#叠加常住人口
data_qingdao.plot(ax=axes3, column='常住人口', alpha=0.5, edgecolor='k',
categorical=False, legend=True, legend_kwds={'loc': 'lower right'},
scheme='User_Defined', classification_kwds=dict(bins=[50,100,150,200,250]),
cmap='OrRd', linewidth=1)
for ctype, data in metro_data_qingdao_all.groupby('name'):
color = Lines_Palette[ctype]
data.plot(ax=axes3, color=color, label=ctype, linewidth=0.5)
cx.add_basemap(ax=axes3, source='https://stamen-tiles-a.a.ssl.fastly.net/terrain-background/{z}/{x}/{y}.png', zoom=8)
axes3.add_artist(ScaleBar(1))
axes3.axis('off')
axes3.set_title('常住人口 (万人)')
#叠加人口密度
data_qingdao.plot(ax=axes4, column='人口密度', alpha=0.5, edgecolor='k',
categorical=False, legend=True, legend_kwds={'loc': 'lower right'},
scheme='User_Defined', classification_kwds=dict(bins=[1000,5000,10000,15000,20000, 30000]),
cmap='OrRd', linewidth=1)
for ctype, data in metro_data_qingdao_all.groupby('name'):
color = Lines_Palette[ctype]
data.plot(ax=axes4, color=color, label=ctype, linewidth=0.5)
cx.add_basemap(ax=axes4, source='https://stamen-tiles-a.a.ssl.fastly.net/terrain-background/{z}/{x}/{y}.png', zoom=8)
axes4.add_artist(ScaleBar(1))
axes4.axis('off')
axes4.set_title('人口密度(人/$km^2$)')
figure.savefig('青岛地铁4-1.png', pad_inches=0, bbox_inches='tight', dpi=300)
